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首页> 外文期刊>Communications in Statistics >A Variable Selection Criterion for Two Sets of Principal Component Scores in Principal Canonical Correlation Analysis
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A Variable Selection Criterion for Two Sets of Principal Component Scores in Principal Canonical Correlation Analysis

机译：主正则相关分析中两组主成分得分的变量选择准则

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摘要

Canonical correlation analysis (CCA) is often used to analyze the correlation between two random vectors. However, sometimes interpretation of CCA results may be hard. In an attempt to address these difficulties, principal canonical correlation analysis (PCCA) was proposed. PCCA is CCA between two sets of principal component (PC) scores. We consider the problem of selecting useful PC scores in CCA. A variable selection criterion for one set of PC scores has been proposed by Ogura (2010), here, we propose a variable selection criterion for two sets of PC scores in PCCA. Furthermore, we demonstrate the effectiveness of this criterion.
机译：典型相关分析（CCA）通常用于分析两个随机向量之间的相关性。但是，有时难以解释CCA结果。为了解决这些困难，提出了主要规范相关分析（PCCA）。 PCCA是两组主要成分（PC）得分之间的CCA。我们考虑在CCA中选择有用的PC分数的问题。 Ogura（2010）提出了一套PC评分的变量选择准则，在这里，我们提出了PCCA中两组PC评分的变量选择准则。此外，我们证明了该标准的有效性。

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